{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import absolute_import\n",
    "from __future__ import division\n",
    "from __future__ import print_function\n",
    "\n",
    "import collections\n",
    "import math\n",
    "import os\n",
    "import random\n",
    "from tempfile import gettempdir\n",
    "import zipfile\n",
    "\n",
    "import numpy as np\n",
    "from six.moves import urllib\n",
    "from six.moves import xrange  # pylint: disable=redefined-builtin\n",
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data size 1903073\n"
     ]
    }
   ],
   "source": [
    "# Step1: Read the data into a list of strings.\n",
    "def read_data(filename):\n",
    "    \"\"\"Extract the first file enclosed in a zip file as a list of words.\"\"\"\n",
    "    with open(filename,'r') as f:\n",
    "         dataraw = f.read()\n",
    "         data=[]\n",
    "         for s in dataraw:\n",
    "                data.append(s)\n",
    "    return data\n",
    "\n",
    "vocabulary = read_data('QuanSongCi.txt')\n",
    "print('Data size', len(vocabulary))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Most common words (+UNK) [['UNK', 1196], ('。', 149620), ('\\n', 117070), ('，', 108451), ('、', 19612), ('人', 13607), ('花', 12809), ('风', 12568), ('一', 11301), ('（', 10967)]\n",
      "Sample data [1503, 1828, 2, 2, 40, 613, 47, 9, 111, 117] ['潘', '阆', '\\n', '\\n', '酒', '泉', '子', '（', '十', '之']\n"
     ]
    }
   ],
   "source": [
    "# Step 2: Build the dictionary and replace rare words with UNK token.\n",
    "vocabulary_size = 5000\n",
    "\n",
    "def build_dataset(words, n_words):\n",
    "  \"\"\"Process raw inputs into a dataset.\"\"\"\n",
    "  count = [['UNK', -1]]\n",
    "  count.extend(collections.Counter(words).most_common(n_words - 1))\n",
    "  dictionary = dict()\n",
    "  for word, _ in count:\n",
    "    dictionary[word] = len(dictionary)\n",
    "  data = list()\n",
    "  unk_count = 0\n",
    "  for word in words:\n",
    "    index = dictionary.get(word, 0)\n",
    "    if index == 0:  # dictionary['UNK']\n",
    "      unk_count += 1\n",
    "    data.append(index)\n",
    "  count[0][1] = unk_count\n",
    "  reversed_dictionary = dict(zip(dictionary.values(), dictionary.keys()))\n",
    "  return data, count, dictionary, reversed_dictionary\n",
    "\n",
    "# Filling 4 global variables:\n",
    "# data - list of codes (integers from 0 to vocabulary_size-1).\n",
    "#   This is the original text but words are replaced by their codes\n",
    "# count - map of words(strings) to count of occurrences\n",
    "# dictionary - map of words(strings) to their codes(integers)\n",
    "# reverse_dictionary - maps codes(integers) to words(strings)\n",
    "data, count, dictionary, reverse_dictionary = build_dataset(vocabulary,\n",
    "                                                            vocabulary_size)\n",
    "del vocabulary  # Hint to reduce memory.\n",
    "print('Most common words (+UNK)', count[:10])\n",
    "print('Sample data', data[:10], [reverse_dictionary[i] for i in data[:10]])\n",
    "\n",
    "data_index = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1828 阆 -> 1503 潘\n",
      "1828 阆 -> 2 \n",
      "\n",
      "2 \n",
      " -> 2 \n",
      "\n",
      "2 \n",
      " -> 1828 阆\n",
      "2 \n",
      " -> 2 \n",
      "\n",
      "2 \n",
      " -> 40 酒\n",
      "40 酒 -> 2 \n",
      "\n",
      "40 酒 -> 613 泉\n"
     ]
    }
   ],
   "source": [
    "# Step 3: Function to generate a training batch for the skip-gram model.\n",
    "def generate_batch(batch_size, num_skips, skip_window):\n",
    "  global data_index\n",
    "  assert batch_size % num_skips == 0\n",
    "  assert num_skips <= 2 * skip_window\n",
    "  batch = np.ndarray(shape=(batch_size), dtype=np.int32)\n",
    "  labels = np.ndarray(shape=(batch_size, 1), dtype=np.int32)\n",
    "  span = 2 * skip_window + 1  # [ skip_window target skip_window ]\n",
    "  buffer = collections.deque(maxlen=span)\n",
    "  if data_index + span > len(data):\n",
    "    data_index = 0\n",
    "  buffer.extend(data[data_index:data_index + span])\n",
    "  data_index += span\n",
    "  for i in range(batch_size // num_skips):\n",
    "    context_words = [w for w in range(span) if w != skip_window]\n",
    "    words_to_use = random.sample(context_words, num_skips)\n",
    "    for j, context_word in enumerate(words_to_use):\n",
    "      batch[i * num_skips + j] = buffer[skip_window]\n",
    "      labels[i * num_skips + j, 0] = buffer[context_word]\n",
    "    if data_index == len(data):\n",
    "      buffer = data[:span]\n",
    "      data_index = span\n",
    "    else:\n",
    "      buffer.append(data[data_index])\n",
    "      data_index += 1\n",
    "  # Backtrack a little bit to avoid skipping words in the end of a batch\n",
    "  data_index = (data_index + len(data) - span) % len(data)\n",
    "  return batch, labels\n",
    "\n",
    "batch, labels = generate_batch(batch_size=8, num_skips=2, skip_window=1)\n",
    "for i in range(8):\n",
    "  print(batch[i], reverse_dictionary[batch[i]],\n",
    "        '->', labels[i, 0], reverse_dictionary[labels[i, 0]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From <ipython-input-5-7700288a5fe6>:56: calling reduce_sum (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "keep_dims is deprecated, use keepdims instead\n"
     ]
    }
   ],
   "source": [
    "# Step 4: Build and train a skip-gram model.\n",
    "batch_size = 128\n",
    "embedding_size = 128  # Dimension of the embedding vector.\n",
    "skip_window = 1       # How many words to consider left and right.\n",
    "num_skips = 2         # How many times to reuse an input to generate a label.\n",
    "num_sampled = 64      # Number of negative examples to sample.\n",
    "\n",
    "# We pick a random validation set to sample nearest neighbors. Here we limit the\n",
    "# validation samples to the words that have a low numeric ID, which by\n",
    "# construction are also the most frequent. These 3 variables are used only for\n",
    "# displaying model accuracy, they don't affect calculation.\n",
    "valid_size = 16     # Random set of words to evaluate similarity on.\n",
    "valid_window = 100  # Only pick dev samples in the head of the distribution.\n",
    "valid_examples = np.random.choice(valid_window, valid_size, replace=False)\n",
    "\n",
    "\n",
    "graph = tf.Graph()\n",
    "\n",
    "with graph.as_default():\n",
    "\n",
    "  # Input data.\n",
    "  train_inputs = tf.placeholder(tf.int32, shape=[batch_size])\n",
    "  train_labels = tf.placeholder(tf.int32, shape=[batch_size, 1])\n",
    "  valid_dataset = tf.constant(valid_examples, dtype=tf.int32)\n",
    "\n",
    "  # Ops and variables pinned to the CPU because of missing GPU implementation\n",
    "  with tf.device('/cpu:0'):\n",
    "    # Look up embeddings for inputs.\n",
    "    embeddings = tf.Variable(\n",
    "        tf.random_uniform([vocabulary_size, embedding_size], -1.0, 1.0))\n",
    "    embed = tf.nn.embedding_lookup(embeddings, train_inputs)\n",
    "\n",
    "    # Construct the variables for the NCE loss\n",
    "    nce_weights = tf.Variable(\n",
    "        tf.truncated_normal([vocabulary_size, embedding_size],\n",
    "                            stddev=1.0 / math.sqrt(embedding_size)))\n",
    "    nce_biases = tf.Variable(tf.zeros([vocabulary_size]))\n",
    "\n",
    "  # Compute the average NCE loss for the batch.\n",
    "  # tf.nce_loss automatically draws a new sample of the negative labels each\n",
    "  # time we evaluate the loss.\n",
    "  # Explanation of the meaning of NCE loss:\n",
    "  #   http://mccormickml.com/2016/04/19/word2vec-tutorial-the-skip-gram-model/\n",
    "  loss = tf.reduce_mean(\n",
    "      tf.nn.nce_loss(weights=nce_weights,\n",
    "                     biases=nce_biases,\n",
    "                     labels=train_labels,\n",
    "                     inputs=embed,\n",
    "                     num_sampled=num_sampled,\n",
    "                     num_classes=vocabulary_size))\n",
    "\n",
    "  # Construct the SGD optimizer using a learning rate of 1.0.\n",
    "  optimizer = tf.train.GradientDescentOptimizer(1.0).minimize(loss)\n",
    "\n",
    "  # Compute the cosine similarity between minibatch examples and all embeddings.\n",
    "  norm = tf.sqrt(tf.reduce_sum(tf.square(embeddings), 1, keep_dims=True))\n",
    "  normalized_embeddings = embeddings / norm\n",
    "  valid_embeddings = tf.nn.embedding_lookup(\n",
    "      normalized_embeddings, valid_dataset)\n",
    "  similarity = tf.matmul(\n",
    "      valid_embeddings, normalized_embeddings, transpose_b=True)\n",
    "\n",
    "  # Add variable initializer.\n",
    "  init = tf.global_variables_initializer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initialized\n",
      "Average loss at step  0 :  195.7344970703125\n",
      "Nearest to 寒: 实, 杰, 嶙, 银, 谣, 撋, 蛙, 凰,\n",
      "Nearest to 年: 渡, 饿, 匮, 芹, 幸, 5, 畴, 嫱,\n",
      "Nearest to 不: 卵, 亏, 睛, 蔗, 犊, 曾, 蒌, 拈,\n",
      "Nearest to 今: 缰, 道, 晋, 徘, 扣, 这, 浅, 吷,\n",
      "Nearest to 明: 逆, 缟, 裟, 矻, 展, 宅, 地, 箭,\n",
      "Nearest to 夜: 莎, 7, 殢, 嫦, 冶, 陶, 浯, 渭,\n",
      "Nearest to 里: 轭, 汗, 守, 抵, 篮, 理, 赪, 乎,\n",
      "Nearest to 江: 绨, 草, 蘋, 浚, 麈, 玦, 永, 舜,\n",
      "Nearest to 更: 爱, 把, 嘘, 侣, 鸩, 箭, 燮, 涴,\n",
      "Nearest to 玉: 瀼, 史, 蕡, 伪, 窬, 腐, 辰, 徜,\n",
      "Nearest to 西: 蘧, 旂, 跳, 续, 滕, 图, 柏, 讲,\n",
      "Nearest to 东: 池, 橹, 盍, 嘱, 嘹, 倏, 脑, 险,\n",
      "Nearest to 来: 赢, 滴, 财, 柜, 阄, 缑, 谯, 腕,\n",
      "Nearest to 日: 睛, 棹, 燃, 圃, 赏, 髦, 元, 侔,\n",
      "Nearest to 老: 朵, 隆, 村, 垂, 相, 复, 蓬, 赋,\n",
      "Nearest to 为: 3, 姥, 浑, 瑰, 剩, 我, 活, 篁,\n",
      "Average loss at step  2000 :  21.44782810330391\n",
      "Average loss at step  4000 :  5.301556235909462\n",
      "Average loss at step  6000 :  4.916902563810348\n",
      "Average loss at step  8000 :  4.6796161196231845\n",
      "Average loss at step  10000 :  4.58848133584857\n",
      "Nearest to 寒: 杰, 银, 萼, 痛, 实, 谣, 蠢, 作,\n",
      "Nearest to 年: 渡, 葫, 饿, 幸, □, 阅, 5, 栊,\n",
      "Nearest to 不: 何, 曾, 犊, 拈, 睛, 自, 髭, 未,\n",
      "Nearest to 今: 这, 道, 晋, 徘, 缰, 浅, 寰, 岘,\n",
      "Nearest to 明: 逆, 地, 缟, 展, 想, 死, 佩, 宗,\n",
      "Nearest to 夜: 莎, 殢, 彭, 嫦, 冶, 收, 积, 渭,\n",
      "Nearest to 里: 理, 乎, 守, 抵, 这, □, 汗, 湲,\n",
      "Nearest to 江: 草, 舜, 暝, 永, 指, 翘, 错, 残,\n",
      "Nearest to 更: 爱, 涴, 侣, 箭, 钓, 宴, 把, 枣,\n",
      "Nearest to 玉: 瀼, 史, 金, 芦, 岭, 母, 以, 麓,\n",
      "Nearest to 西: 蘧, 图, 渐, 续, 柏, 咨, 慈, 跳,\n",
      "Nearest to 东: 池, 盍, 险, 橹, 鹧, 乙, 脑, 关,\n",
      "Nearest to 来: 滴, 赢, 鸣,  , 怨, 逆, 亳, 娘,\n",
      "Nearest to 日: 睛, 圃, 燃, 赏, 翚, 元, 棹, 骅,\n",
      "Nearest to 老: 朵, 隆, 村, 相, 赋, 复, 紧, 蓬,\n",
      "Nearest to 为: 3, 剩, 浑, 瑰, 株, 活, 我, 尾,\n",
      "Average loss at step  12000 :  4.570360408306122\n",
      "Average loss at step  14000 :  4.44489131128788\n",
      "Average loss at step  16000 :  4.457518062710762\n",
      "Average loss at step  18000 :  4.500773637413978\n",
      "Average loss at step  20000 :  4.444541262507439\n",
      "Nearest to 寒: 杰, 嶙, 风, 银, 谣, 蠢, 痛, 实,\n",
      "Nearest to 年: 葫, 阅, 饿, 渡, 畴, 幸, 栊, 辚,\n",
      "Nearest to 不: 何, 未, 曾, 犊, 矗, 睛, 岷, 髭,\n",
      "Nearest to 今: 这, 道, 晋, 徘, 岘, 缰, 涴, 元,\n",
      "Nearest to 明: 逆, 地, 想, 缟, 展, 8, 宗, 泫,\n",
      "Nearest to 夜: 殢, 积, 嫦, 后, 挈, 彭, 收, 冶,\n",
      "Nearest to 里: 理, 乎, 顷, 湲, 醽, 读, 徊, 守,\n",
      "Nearest to 江: 舜, 草, 暝, 水, 绨, 翘, 永, 市,\n",
      "Nearest to 更: 涴, 爱, 箭, 宴, 侣, 泮, 右, 把,\n",
      "Nearest to 玉: 史, 瀼, 金, 芦, 岭, 腐, 土, 饰,\n",
      "Nearest to 西: 蘧, 慈, 柏, 咨, 渐, 北, 祷, 图,\n",
      "Nearest to 东: 池, 险, 盍, 鹧, 脑, 橹, 关, 北,\n",
      "Nearest to 来: 逆, 后, 怨, 醴, 鸣, 娘, 便, 去,\n",
      "Nearest to 日: 睛, 燃, 圃, 赏, 月, 骅, 翚, 十,\n",
      "Nearest to 老: 隆, 朵, 紧, 相, 赋, 酴, 复, 稚,\n",
      "Nearest to 为: 3, 剩, 姥, 寿, 活, 善, 株, 浑,\n",
      "Average loss at step  22000 :  4.389113949537277\n",
      "Average loss at step  24000 :  4.351145897269249\n",
      "Average loss at step  26000 :  4.3619451488256455\n",
      "Average loss at step  28000 :  4.409069263339043\n",
      "Average loss at step  30000 :  4.319983925223351\n",
      "Nearest to 寒: 杰, 静, 银, 嶙, 实, 风, 谣, 晴,\n",
      "Nearest to 年: 阅, 岁, 葫, 畴, 栊, 饿, 辚, 牟,\n",
      "Nearest to 不: 未, 何, 矗, 曾, 犊, 睛, 睍, 无,\n",
      "Nearest to 今: 这, 道, 晋, 岘, 元, 涴, 缰, 徘,\n",
      "Nearest to 明: 逆, 8, 宗, 辄, 缟, 泫, 展, 晓,\n",
      "Nearest to 夜: 殢, 积, 后, 彭, 瀹, 吊, 挈, 嫦,\n",
      "Nearest to 里: 理, 顷, 乎, 湲, 这, 醽, 读, 串,\n",
      "Nearest to 江: 绨, 水, 暝, 舜, 草, 浚, 仪, 翘,\n",
      "Nearest to 更: 涴, 爱, 箭, 右, 泮, 侣, 醅, 宴,\n",
      "Nearest to 玉: 金, 史, 瀼, 芦, 岭, 土, 腐, 饰,\n",
      "Nearest to 西: 蘧, 东, 咨, 南, 慈, 祷, 柏, 北,\n",
      "Nearest to 东: 池, 西, 险, 薰, 北, 盍, 脑, 鹧,\n",
      "Nearest to 来: 去, 后, 怨, 饿, 便, 财, 娘, 逆,\n",
      "Nearest to 日: 月, 燃, 睛, 圃, 骅, 赏, 趱, 侔,\n",
      "Nearest to 老: 隆, 朵, 紧, 赋, 挼, 稚, 复, 少,\n",
      "Nearest to 为: 与, 3, 寿, 姥, 活, 剩, 互, 善,\n",
      "Average loss at step  32000 :  4.16483436691761\n",
      "Average loss at step  34000 :  4.203520514249802\n",
      "Average loss at step  36000 :  4.229901592493057\n",
      "Average loss at step  38000 :  4.1932634044885635\n",
      "Average loss at step  40000 :  4.181887204408645\n",
      "Nearest to 寒: 杰, 晴, 嶙, 静, 蠢, 盐, 溶, 风,\n",
      "Nearest to 年: 岁, 畴, 阅, 葫, 牟, 饿, 辚, 镰,\n",
      "Nearest to 不: 未, 何, 矗, 无, 谁, 犊, 喘, 包,\n",
      "Nearest to 今: 这, 元, 涴, 晋, 道, 永, 来, 岘,\n",
      "Nearest to 明: 8, 逆, 宗, 晓, 缟, 辄, 泫, 展,\n",
      "Nearest to 夜: 后, 吊, 挈, 积, 耿, 敞, 峙, 凉,\n",
      "Nearest to 里: 顷, 理, 湲, 乎, 拥, 徊, 串, □,\n",
      "Nearest to 江: 水, 绨, 舜, 浚, 草, 淮, 仪, 玻,\n",
      "Nearest to 更: 涴, 爱, 箭, 右, 泮, 处, 渐, 释,\n",
      "Nearest to 玉: 史, 金, 瀼, 琼, 麟, 宝, 土, 饰,\n",
      "Nearest to 西: 东, 北, 南, 蘧, 祷, 慈, 咨, 吹,\n",
      "Nearest to 东: 西, 池, 北, 险, 薰, 市, 巨, 盍,\n",
      "Nearest to 来: 去, 后, 便, 怡, 怨, 醴, 财, 饿,\n",
      "Nearest to 日: 燃, 月, 圃, 睛, 骅, 赏, 趱, 侔,\n",
      "Nearest to 老: 隆, 紧, 朵, 赋, 敝, 谭, 复, 稚,\n",
      "Nearest to 为: 与, 寿, 3, 姥, 互, 活, 剩, 善,\n",
      "Average loss at step  42000 :  4.211921427965164\n",
      "Average loss at step  44000 :  4.20029970729351\n",
      "Average loss at step  46000 :  4.240730224370957\n",
      "Average loss at step  48000 :  4.281063437104225\n",
      "Average loss at step  50000 :  4.256812403678894\n",
      "Nearest to 寒: 晴, 杰, 风, 静, 嶙, 溶, 盐, 蠢,\n",
      "Nearest to 年: 岁, 畴, 阅, 牟, 葫, 辚, 栊, 撑,\n",
      "Nearest to 不: 未, 何, 矗, 技, 包, 无, 否, 齑,\n",
      "Nearest to 今: 这, 元, 涴, 来, 岘, 昨, 葫, 永,\n",
      "Nearest to 明: 8, 逆, 宗, 辄, 榔, 晓, 潦, 展,\n",
      "Nearest to 夜: 后, 敞, 积, 吊, 挈, 耿, 峙, 训,\n",
      "Nearest to 里: 顷, 理, 湲, 拥, 户, 醽, 徊, 乎,\n",
      "Nearest to 江: 水, 绨, 浚, 舜, 泗, 市, 、, 岸,\n",
      "Nearest to 更: 涴, 箭, 泮, 爱, 右, 共, 处, 灌,\n",
      "Nearest to 玉: 金, 史, 琼, 瀼, 宝, 阓, 麟, 隅,\n",
      "Nearest to 西: 东, 南, 北, 慈, 祷, 蘧, 吹, 薰,\n",
      "Nearest to 东: 西, 北, 薰, 池, 险, 市, 巨, 脑,\n",
      "Nearest to 来: 去, 后, 醴, 栾, 怡, 财, 摺, 今,\n",
      "Nearest to 日: 月, 燃, 骅, 圃, 睛, 趱, 赏, 侔,\n",
      "Nearest to 老: 隆, 紧, 朵, 敝, 稚, 庚, 谭, 博,\n",
      "Nearest to 为: 与, 寿, 互, 3, 善, 是, 活, 姥,\n",
      "Average loss at step  52000 :  4.2432255654335025\n",
      "Average loss at step  54000 :  4.197601226210594\n",
      "Average loss at step  56000 :  4.232066300988198\n",
      "Average loss at step  58000 :  4.254579465985298\n",
      "Average loss at step  60000 :  4.177051020026207\n",
      "Nearest to 寒: 晴, 秋, 静, 杰, 盐, 溶, 芙, 暖,\n",
      "Nearest to 年: 岁, 畴, 阅, 牟, 时, 栊, 谩, 撑,\n",
      "Nearest to 不: 未, 何, 无, 矗, 技, 也, 歃, 难,\n",
      "Nearest to 今: 这, 昨, 元, 来, 此, 永, 涴, 葫,\n",
      "Nearest to 明: 8, 晓, 辄, 宗, 榔, 聘, 逆, 展,\n",
      "Nearest to 夜: 宵, 敞, 夕, 后, 吊, 积, 焘, 峙,\n",
      "Nearest to 里: 顷, 理, 湲, 拥, 中, 户, 醽, 徊,\n",
      "Nearest to 江: 水, 绨, 麈, 溪, 淮, 蘋, 仪, 岸,\n",
      "Nearest to 更: 涴, 右, 箭, 爱, 泮, 共, 耳, 待,\n",
      "Nearest to 玉: 琼, 金, 瀼, 史, 宝, 麟, 阓, 冰,\n",
      "Nearest to 西: 东, 南, 北, 慈, 吹, 蘧, 祷, 奄,\n",
      "Nearest to 东: 西, 北, 薰, 险, 池, 巨, 王, 洛,\n",
      "Nearest to 来: 去, 今, 后, 栾, 怡, 便, 饿, 摺,\n",
      "Nearest to 日: 月, 燃, 骅, 圃, 年, 趱, 侔, 尽,\n",
      "Nearest to 老: 隆, 紧, 朵, 敝, 博, 谭, 稚, 昉,\n",
      "Nearest to 为: 与, 寿, 互, 伴, 活, 同, 刹, 剩,\n",
      "Average loss at step  62000 :  4.095874022960663\n",
      "Average loss at step  64000 :  4.1132104022502896\n",
      "Average loss at step  66000 :  4.130594591140747\n",
      "Average loss at step  68000 :  4.110580813169479\n",
      "Average loss at step  70000 :  4.106645888388157\n",
      "Nearest to 寒: 晴, 秋, 静, 溶, 盐, 杰, 芙, 蠢,\n",
      "Nearest to 年: 岁, 畴, 牟, 阅, 妥, 撑, 时, 辚,\n",
      "Nearest to 不: 未, 何, 矗, 无, 谁, 难, 技, 莫,\n",
      "Nearest to 今: 来, 这, 昨, 元, 此, 永, 涴, 葫,\n",
      "Nearest to 明: 8, 晓, 宗, 榔, 辄, 特, 聘, 展,\n",
      "Nearest to 夜: 宵, 夕, 后, 敞, 焘, 吊, 峙, 晓,\n",
      "Nearest to 里: 顷, 拥, 湲, 中, 理, 户, 倬, 醽,\n",
      "Nearest to 江: 水, 淮, 绨, 溪, 麈, 玻, 舜, 泗,\n",
      "Nearest to 更: 涴, 箭, 右, 泮, 渐, 处, 灌, 共,\n",
      "Nearest to 玉: 琼, 金, 史, 瀼, 宝, 麟, 冰, 瑶,\n",
      "Nearest to 西: 东, 南, 北, 吹, 蘧, 慈, 奄, 祷,\n",
      "Nearest to 东: 西, 北, 险, 薰, 池, 巨, 市, 王,\n",
      "Nearest to 来: 去, 今, 怡, 便, 醴, 栾, 摺, 财,\n",
      "Nearest to 日: 燃, 月, 圃, 骅, 趱, 年, 惨, 夕,\n",
      "Nearest to 老: 紧, 隆, 敝, 博, 好, 朵, 谭, 归,\n",
      "Nearest to 为: 与, 伴, 寿, 互, 刹, 活, 亦, 善,\n",
      "Average loss at step  72000 :  4.124896617174149\n",
      "Average loss at step  74000 :  4.124286943912506\n",
      "Average loss at step  76000 :  4.195658674359321\n",
      "Average loss at step  78000 :  4.197977839350701\n",
      "Average loss at step  80000 :  4.190028324007988\n",
      "Nearest to 寒: 晴, 秋, 溶, 暖, 盐, 静, 霜, 蠢,\n",
      "Nearest to 年: 畴, 岁, 牟, 阅, 时, 撑, 妥, 辚,\n",
      "Nearest to 不: 未, 何, 矗, 技, 难, 否, 无, 包,\n",
      "Nearest to 今: 昨, 这, 来, 此, 元, 许, 涴, 葫,\n",
      "Nearest to 明: 8, 晓, 榔, 宗, 辄, 当, 特, 平,\n",
      "Nearest to 夜: 宵, 夕, 敞, 焘, 后, 峙, 训, 晓,\n",
      "Nearest to 里: 顷, 中, 湲, 拥, 理, 户, 卉, 醽,\n",
      "Nearest to 江: 水, 绨, 溪, 泗, 淮, 湘, 乱, 麈,\n",
      "Nearest to 更: 涴, 泮, 共, 箭, 待, 便, 又, 渐,\n",
      "Nearest to 玉: 琼, 金, 宝, 冰, 瀼, 阓, 瑶, 史,\n",
      "Nearest to 西: 东, 南, 北, 吹, 慈, 奄, 晓, 蘧,\n",
      "Nearest to 东: 西, 北, 薰, 险, 巨, 池, 糜, 洛,\n",
      "Nearest to 来: 去, 今, 后, 栾, 怡, 摺, 醴, 财,\n",
      "Nearest to 日: 月, 燃, 年, 夕, 骅, 趱, 圃, 惨,\n",
      "Nearest to 老: 紧, 隆, 敝, 昉, 博, 稚, 谭, 庚,\n",
      "Nearest to 为: 与, 伴, 同, 亦, 赠, 互, 是, 寿,\n",
      "Average loss at step  82000 :  4.171774692296982\n",
      "Average loss at step  84000 :  4.137443061113357\n",
      "Average loss at step  86000 :  4.185088517904282\n",
      "Average loss at step  88000 :  4.177842560172081\n",
      "Average loss at step  90000 :  4.099728273153305\n",
      "Nearest to 寒: 秋, 晴, 盐, 静, 残, 暖, 溶, 霜,\n",
      "Nearest to 年: 岁, 畴, 牟, 阅, 时, 妥, 日, 撑,\n",
      "Nearest to 不: 未, 何, 无, 技, 难, 歃, 也, 否,\n",
      "Nearest to 今: 昨, 此, 来, 元, 这, 许, 久, 永,\n",
      "Nearest to 明: 晓, 8, 当, 辄, 榔, 光, 宗, 照,\n",
      "Nearest to 夜: 宵, 敞, 夕, 焘, 后, 晓, 峙, 吊,\n",
      "Nearest to 里: 顷, 中, 拥, 湲, 入, 户, 理, 绕,\n",
      "Nearest to 江: 水, 溪, 麈, 湘, 淮, 绨, 泗, 蘋,\n",
      "Nearest to 更: 涴, 便, 共, 待, 又, 泮, 处, 右,\n",
      "Nearest to 玉: 琼, 金, 宝, 瑶, 麟, 冰, 阓, 瀼,\n",
      "Nearest to 西: 东, 南, 北, 吹, 奄, 慈, 祷, 蘧,\n",
      "Nearest to 东: 西, 北, 薰, 险, 巨, 池, 洛, 糜,\n",
      "Nearest to 来: 去, 今, 栾, 又, 后, 怡, 摺, 归,\n",
      "Nearest to 日: 月, 燃, 年, 趱, 圃, 骅, 夕, 怕,\n",
      "Nearest to 老: 紧, 隆, 敝, 昉, 博, 谭, 稚, 搏,\n",
      "Nearest to 为: 与, 同, 伴, 共, 赠, 似, 在, 刹,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average loss at step  92000 :  4.049722226500511\n",
      "Average loss at step  94000 :  4.082611269593238\n",
      "Average loss at step  96000 :  4.055503833293915\n",
      "Average loss at step  98000 :  4.081341600298882\n",
      "Average loss at step  100000 :  4.053135705530644\n",
      "Nearest to 寒: 秋, 晴, 溶, 盐, 静, 霜, 暖, 残,\n",
      "Nearest to 年: 岁, 畴, 牟, 阅, 妥, 时, 日, 撑,\n",
      "Nearest to 不: 未, 何, 无, 难, 技, 莫, 矗, 谁,\n",
      "Nearest to 今: 昨, 来, 许, 元, 此, 他, 这, 久,\n",
      "Nearest to 明: 晓, 8, 当, 宗, 榔, 辄, 特, 照,\n",
      "Nearest to 夜: 宵, 夕, 焘, 敞, 后, 峙, 晓, 训,\n",
      "Nearest to 里: 中, 拥, 顷, 入, 湲, 绕, 倬, 理,\n",
      "Nearest to 江: 水, 淮, 溪, 湘, 麈, 泗, 海, 绨,\n",
      "Nearest to 更: 涴, 便, 又, 处, 共, 渐, 待, 泮,\n",
      "Nearest to 玉: 琼, 金, 宝, 瑶, 冰, 瀼, 麟, 史,\n",
      "Nearest to 西: 东, 南, 北, 奄, 吹, 蘧, 慈, 晤,\n",
      "Nearest to 东: 西, 北, 险, 薰, 巨, 糜, 市, 洛,\n",
      "Nearest to 来: 去, 今, 怡, 栾, 摺, 又, 醴, 便,\n",
      "Nearest to 日: 燃, 月, 年, 夕, 圃, 时, 骅, 趱,\n",
      "Nearest to 老: 紧, 隆, 敝, 博, 好, 昉, 归, 妇,\n",
      "Nearest to 为: 与, 伴, 同, 词, 共, 赠, 因, 亦,\n",
      "Average loss at step  102000 :  4.079227083444596\n",
      "Average loss at step  104000 :  4.093347291350365\n",
      "Average loss at step  106000 :  4.147647060036659\n",
      "Average loss at step  108000 :  4.148791678190231\n",
      "Average loss at step  110000 :  4.166635084271431\n",
      "Nearest to 寒: 秋, 晴, 霜, 暖, 溶, 盐, 暝, 残,\n",
      "Nearest to 年: 岁, 畴, 牟, 阅, 缵, 时, 日, 撑,\n",
      "Nearest to 不: 未, 何, 难, 技, 否, 歃, 包, 无,\n",
      "Nearest to 今: 昨, 此, 许, 来, 元, 他, 这, 岘,\n",
      "Nearest to 明: 8, 当, 榔, 晓, 宗, 辄, 特, 皎,\n",
      "Nearest to 夜: 宵, 夕, 焘, 敞, 后, 峙, 训, 晓,\n",
      "Nearest to 里: 中, 拥, 顷, 入, 绕, 湲, 理, 户,\n",
      "Nearest to 江: 水, 溪, 淮, 湘, 泗, 乱, 麈, 愁,\n",
      "Nearest to 更: 涴, 便, 又, 泮, 共, 待, 箭, 处,\n",
      "Nearest to 玉: 琼, 宝, 金, 冰, 瑶, 阓, 瀼, 麟,\n",
      "Nearest to 西: 东, 南, 北, 奄, 吹, 晤, 慈, 斜,\n",
      "Nearest to 东: 西, 北, 薰, 险, 巨, 糜, 洛, 市,\n",
      "Nearest to 来: 去, 今, 栾, 摺, 怡, 醴, 饿, 又,\n",
      "Nearest to 日: 月, 燃, 夕, 年, 朝, 时, 又, 怕,\n",
      "Nearest to 老: 紧, 隆, 昉, 搏, 敝, 谭, 归, 稚,\n",
      "Nearest to 为: 与, 同, 赠, 亦, 送, 似, 共, 伴,\n",
      "Average loss at step  112000 :  4.116621723294258\n",
      "Average loss at step  114000 :  4.105391306757927\n",
      "Average loss at step  116000 :  4.143855878829956\n",
      "Average loss at step  118000 :  4.1362495206594465\n",
      "Average loss at step  120000 :  4.032156405210495\n",
      "Nearest to 寒: 秋, 晴, 残, 暝, 暖, 霜, 盐, 溶,\n",
      "Nearest to 年: 岁, 畴, 牟, 时, 阅, 妥, 缵, 撑,\n",
      "Nearest to 不: 未, 技, 无, 难, 何, 歃, 也, 否,\n",
      "Nearest to 今: 昨, 此, 元, 许, 来, 他, 这, 久,\n",
      "Nearest to 明: 晓, 8, 当, 光, 辄, 榔, 宗, 皎,\n",
      "Nearest to 夜: 宵, 夕, 敞, 焘, 后, 晓, 峙, 更,\n",
      "Nearest to 里: 中, 拥, 入, 顷, 绕, 湲, 户, 倬,\n",
      "Nearest to 江: 水, 溪, 淮, 湘, 麈, 潇, 潭, 泗,\n",
      "Nearest to 更: 便, 又, 共, 涴, 待, 处, 泮, 渐,\n",
      "Nearest to 玉: 琼, 金, 宝, 瑶, 冰, 麟, 阓, 瀼,\n",
      "Nearest to 西: 东, 南, 北, 吹, 奄, 晤, 斜, 慈,\n",
      "Nearest to 东: 西, 北, 薰, 险, 巨, 洛, 糜, 急,\n",
      "Nearest to 来: 去, 又, 今, 栾, 归, 怡, 饿, 摺,\n",
      "Nearest to 日: 月, 年, 燃, 怕, 夕, 又, 朝, 藤,\n",
      "Nearest to 老: 紧, 昉, 搏, 隆, 敝, 谭, 博, 妇,\n",
      "Nearest to 为: 与, 同, 共, 似, 伴, 因, 赠, 词,\n",
      "Average loss at step  122000 :  4.033573855996132\n",
      "Average loss at step  124000 :  4.063255758166314\n",
      "Average loss at step  126000 :  4.008145493030548\n",
      "Average loss at step  128000 :  4.041256549715996\n",
      "Average loss at step  130000 :  4.04635110282898\n",
      "Nearest to 寒: 秋, 晴, 溶, 暖, 暝, 霜, 静, 盐,\n",
      "Nearest to 年: 岁, 畴, 牟, 阅, 时, 缵, 撑, 釂,\n",
      "Nearest to 不: 未, 何, 无, 难, 也, 莫, 歃, 技,\n",
      "Nearest to 今: 昨, 此, 许, 元, 他, 来, 明, 算,\n",
      "Nearest to 明: 晓, 8, 当, 榔, 宗, 照, 今, 聂,\n",
      "Nearest to 夜: 宵, 夕, 焘, 敞, 后, 更, 峙, 晓,\n",
      "Nearest to 里: 中, 拥, 入, 绕, 顷, 湲, 卉, 倬,\n",
      "Nearest to 江: 水, 淮, 溪, 湘, 麈, 泗, 潇, 潭,\n",
      "Nearest to 更: 便, 又, 共, 涴, 处, 滚, 渐, x,\n",
      "Nearest to 玉: 琼, 金, 瑶, 宝, 冰, 瀼, 麟, 獠,\n",
      "Nearest to 西: 东, 南, 北, 奄, 晤, 荦, 蘧, 吹,\n",
      "Nearest to 东: 西, 北, 险, 糜, 巨, 薰, 洛, 急,\n",
      "Nearest to 来: 去, 今, 怡, 又, 栾, 饿, 摺, 衱,\n",
      "Nearest to 日: 月, 燃, 年, 夕, 朝, 又, 呜, 圃,\n",
      "Nearest to 老: 紧, 归, 妇, 隆, 博, 昉, 好, 哲,\n",
      "Nearest to 为: 与, 伴, 词, 共, 同, 因, 赠, 亦,\n",
      "Average loss at step  132000 :  4.039844533443451\n",
      "Average loss at step  134000 :  4.064810057520867\n",
      "Average loss at step  136000 :  4.118860183477402\n",
      "Average loss at step  138000 :  4.112857944965363\n",
      "Average loss at step  140000 :  4.134451882004738\n",
      "Nearest to 寒: 秋, 晴, 暝, 暖, 霜, 溶, 残, 盐,\n",
      "Nearest to 年: 岁, 畴, 缵, 牟, 阅, 撑, 时, 日,\n",
      "Nearest to 不: 未, 难, 歃, 否, 技, 何, 无, 包,\n",
      "Nearest to 今: 昨, 许, 此, 他, 元, 来, 愆, 算,\n",
      "Nearest to 明: 当, 8, 榔, 晓, 皎, 宗, 辄, 照,\n",
      "Nearest to 夜: 宵, 夕, 焘, 敞, 后, 缔, 峙, 对,\n",
      "Nearest to 里: 中, 入, 拥, 绕, 顷, 户, 理, 卉,\n",
      "Nearest to 江: 水, 淮, 溪, 湘, 泗, 潇, 潭, 麈,\n",
      "Nearest to 更: 便, 又, 涴, 共, 泮, 处, 正, 待,\n",
      "Nearest to 玉: 琼, 金, 宝, 瑶, 冰, 阓, 瀼, 獠,\n",
      "Nearest to 西: 东, 南, 北, 晤, 奄, 斜, 吹, 荦,\n",
      "Nearest to 东: 西, 北, 薰, 糜, 巨, 险, 急, 洛,\n",
      "Nearest to 来: 去, 今, 又, 栾, 语, 怡, 饿, 摺,\n",
      "Nearest to 日: 月, 夕, 朝, 燃, 年, 又, 怕, 时,\n",
      "Nearest to 老: 紧, 昉, 搏, 谭, 妇, 稚, 哲, 觉,\n",
      "Nearest to 为: 与, 同, 赠, 似, 共, 因, 亦, 词,\n",
      "Average loss at step  142000 :  4.097665426254273\n",
      "Average loss at step  144000 :  4.071991653323174\n",
      "Average loss at step  146000 :  4.126568595409394\n",
      "Average loss at step  148000 :  4.107164349555969\n",
      "Average loss at step  150000 :  3.9901362923383714\n",
      "Nearest to 寒: 秋, 晴, 暝, 残, 暖, 霜, 黏, 盐,\n",
      "Nearest to 年: 岁, 畴, 牟, 时, 缵, 阅, 妥, 釂,\n",
      "Nearest to 不: 未, 无, 难, 技, 歃, 也, 何, 羞,\n",
      "Nearest to 今: 昨, 此, 元, 许, 来, 他, 愆, 亳,\n",
      "Nearest to 明: 晓, 8, 当, 光, 皎, 辄, 宗, 榔,\n",
      "Nearest to 夜: 宵, 夕, 敞, 焘, 峙, 对, 后, 晓,\n",
      "Nearest to 里: 中, 入, 拥, 绕, 顷, 户, 穴, 湲,\n",
      "Nearest to 江: 水, 溪, 淮, 湘, 麈, 潇, 潭, 泗,\n",
      "Nearest to 更: 便, 又, 共, 正, 涴, 待, 处, 最,\n",
      "Nearest to 玉: 琼, 金, 瑶, 宝, 冰, 麟, 阓, 凝,\n",
      "Nearest to 西: 东, 南, 北, 奄, 晤, 吹, 斜, 临,\n",
      "Nearest to 东: 西, 北, 薰, 巨, 险, 糜, 急, 洛,\n",
      "Nearest to 来: 去, 又, 今, 栾, 饿, 惊, ，, 语,\n",
      "Nearest to 日: 月, 夕, 怕, 年, 朝, 又, 燃, 藤,\n",
      "Nearest to 老: 紧, 昉, 搏, 哲, 妇, 稚, 摧, 谭,\n",
      "Nearest to 为: 与, 同, 共, 因, 似, 赠, 词, 伴,\n",
      "Average loss at step  152000 :  4.010120039701461\n",
      "Average loss at step  154000 :  4.0385573049783705\n",
      "Average loss at step  156000 :  3.985222965836525\n",
      "Average loss at step  158000 :  4.023253185868263\n",
      "Average loss at step  160000 :  4.02247805339098\n",
      "Nearest to 寒: 秋, 晴, 暝, 暖, 溶, 霜, 黏, 残,\n",
      "Nearest to 年: 岁, 畴, 牟, 缵, 时, 阅, 撑, 釂,\n",
      "Nearest to 不: 未, 难, 何, 无, 却, 歃, 莫, 技,\n",
      "Nearest to 今: 昨, 此, 许, 他, 元, 来, 亳, 算,\n",
      "Nearest to 明: 晓, 8, 当, 皎, 照, 榔, 宗, 聂,\n",
      "Nearest to 夜: 宵, 夕, 焘, 敞, 对, 峙, 后, 更,\n",
      "Nearest to 里: 中, 入, 拥, 绕, 湲, 与, 顷, 倬,\n",
      "Nearest to 江: 水, 淮, 溪, 湘, 泗, 麈, 潭, 潇,\n",
      "Nearest to 更: 便, 又, 共, 最, 处, x, 涴, 正,\n",
      "Nearest to 玉: 琼, 金, 瑶, 宝, 獠, 冰, 瀼, 麟,\n",
      "Nearest to 西: 东, 南, 北, 奄, 晤, 荦, 浔, 临,\n",
      "Nearest to 东: 西, 北, 糜, 巨, 险, 洛, 急, 薰,\n",
      "Nearest to 来: 去, 今, 又, 栾, 怡, 摺, 茏, 饿,\n",
      "Nearest to 日: 月, 燃, 夕, 年, 朝, 姥, 又, 述,\n",
      "Nearest to 老: 紧, 哲, 妇, 昉, 搏, 博, 稚, 归,\n",
      "Nearest to 为: 与, 因, 词, 共, 亦, 伴, 同, 似,\n",
      "Average loss at step  162000 :  4.008913781166076\n",
      "Average loss at step  164000 :  4.051598790049553\n",
      "Average loss at step  166000 :  4.081804079890251\n",
      "Average loss at step  168000 :  4.0840489641427995\n",
      "Average loss at step  170000 :  4.105565370678901\n",
      "Nearest to 寒: 秋, 暝, 暖, 溶, 霜, 残, 黏, 晴,\n",
      "Nearest to 年: 岁, 畴, 缵, 时, 牟, 撑, 阅, 釂,\n",
      "Nearest to 不: 未, 难, 歃, 否, 技, 无, 包, 莫,\n",
      "Nearest to 今: 昨, 许, 此, 他, 元, 愆, 来, 明,\n",
      "Nearest to 明: 皎, 当, 8, 榔, 晓, 聂, 圣, 照,\n",
      "Nearest to 夜: 宵, 夕, 焘, 敞, 对, 后, 缔, 峙,\n",
      "Nearest to 里: 入, 中, 绕, 拥, 顷, 与, 穴, 卉,\n",
      "Nearest to 江: 水, 淮, 溪, 泗, 湘, 潭, 波, 潇,\n",
      "Nearest to 更: 便, 又, 共, 正, 涴, 处, 最, 径,\n",
      "Nearest to 玉: 琼, 金, 瑶, 宝, 冰, 阓, 瀼, 诸,\n",
      "Nearest to 西: 东, 南, 北, 晤, 奄, 斜, 荦, 浔,\n",
      "Nearest to 东: 西, 北, 薰, 糜, 巨, 急, 险, 洛,\n",
      "Nearest to 来: 去, 又, 今, 语, 栾, 摺, 耄, 饿,\n",
      "Nearest to 日: 夕, 月, 朝, 燃, 年, 又, 怕, 姥,\n",
      "Nearest to 老: 紧, 昉, 搏, 觉, 哲, 惆, 稚, 妇,\n",
      "Nearest to 为: 与, 因, 亦, 赠, 同, 共, 似, 词,\n",
      "Average loss at step  172000 :  4.087387830853462\n",
      "Average loss at step  174000 :  4.051077401161193\n",
      "Average loss at step  176000 :  4.106037539005279\n",
      "Average loss at step  178000 :  4.082749699950218\n",
      "Average loss at step  180000 :  3.9471405076980592\n",
      "Nearest to 寒: 秋, 暝, 黏, 暖, 晴, 残, 凉, 探,\n",
      "Nearest to 年: 岁, 畴, 时, 牟, 缵, 妥, 阅, 釂,\n",
      "Nearest to 不: 未, 无, 难, 技, 歃, 何, 羞, 否,\n",
      "Nearest to 今: 昨, 此, 许, 元, 他, 来, 愆, 明,\n",
      "Nearest to 明: 晓, 8, 皎, 光, 当, 聂, 今, 宗,\n",
      "Nearest to 夜: 宵, 夕, 敞, 焘, 对, 峙, 日, 后,\n",
      "Nearest to 里: 中, 入, 拥, 绕, 顷, 穴, 与, 语,\n",
      "Nearest to 江: 水, 溪, 淮, 湘, 麈, 潭, 潇, 泗,\n",
      "Nearest to 更: 便, 又, 共, 正, 最, 处, 且, 待,\n",
      "Nearest to 玉: 琼, 金, 瑶, 宝, 冰, 阓, 佳, 獠,\n",
      "Nearest to 西: 东, 南, 北, 奄, 晤, 浔, 斜, 临,\n",
      "Nearest to 东: 西, 北, 巨, 险, 洛, 薰, 糜, 急,\n",
      "Nearest to 来: 去, 又, 今, 栾, 茏, 语, 还, 归,\n",
      "Nearest to 日: 月, 夕, 朝, 怕, 年, 燃, 藤, 又,\n",
      "Nearest to 老: 紧, 搏, 哲, 昉, 妇, 摧, 稚, 非,\n",
      "Nearest to 为: 与, 因, 共, 同, 似, 词, 赠, 亦,\n",
      "Average loss at step  182000 :  4.0051769690513614\n",
      "Average loss at step  184000 :  4.023552205443382\n",
      "Average loss at step  186000 :  3.9610831662416457\n",
      "Average loss at step  188000 :  4.001735919356346\n",
      "Average loss at step  190000 :  4.013946536481381\n",
      "Nearest to 寒: 秋, 暝, 黏, 溶, 暖, 凉, 晴, 冷,\n",
      "Nearest to 年: 岁, 畴, □, 缵, 时, 牟, 日, 阅,\n",
      "Nearest to 不: 未, 何, 无, 难, 莫, 歃, 却, 滚,\n",
      "Nearest to 今: 昨, 他, 此, 许, 元, □, 算, 愆,\n",
      "Nearest to 明: 8, 皎, 照, 晓, 宗, 当, 今, 聂,\n",
      "Nearest to 夜: 宵, 夕, 焘, 敞, 后, 对, 峙, 逮,\n",
      "Nearest to 里: 中, 入, 拥, 绕, □, 与, 顷, 穴,\n",
      "Nearest to 江: 水, 淮, 溪, □, 湘, 泗, 潭, 潇,\n",
      "Nearest to 更: 便, 又, 共, 最, 正, 处, 但, x,\n",
      "Nearest to 玉: 琼, 瑶, 金, 宝, 冰, 獠, 瀼, 阓,\n",
      "Nearest to 西: 东, 南, 北, 晤, 奄, 浔, □, 临,\n",
      "Nearest to 东: 西, 北, □, 糜, 巨, 险, 急, 洛,\n",
      "Nearest to 来: 去, 又, 今, 还, 语, 茏, 摺, 耄,\n",
      "Nearest to 日: 朝, 月, 年, 燃, 夕, □, 姥, 又,\n",
      "Nearest to 老: 哲, 妇, 紧, 昉, 搏, 博, 摧, 稚,\n",
      "Nearest to 为: 与, 因, 词, 共, 亦, 赠, 伴, 同,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average loss at step  192000 :  4.002352433681488\n",
      "Average loss at step  194000 :  4.027360880374909\n",
      "Average loss at step  196000 :  4.072497001171112\n",
      "Average loss at step  198000 :  4.066296860218048\n",
      "Average loss at step  200000 :  4.087049823045731\n",
      "Nearest to 寒: 秋, 暝, 暖, 黏, 冷, 溶, 霜, 凉,\n",
      "Nearest to 年: 岁, 畴, 缵, 时, 撑, 牟, 日, 釂,\n",
      "Nearest to 不: 未, 难, 歃, 也, 否, 莫, 技, 何,\n",
      "Nearest to 今: 昨, 许, 此, 他, 愆, 元, 明, 算,\n",
      "Nearest to 明: 皎, 当, 榔, 今, 8, 圣, 聂, 宗,\n",
      "Nearest to 夜: 宵, 夕, 焘, 敞, 后, 缔, 对, 峙,\n",
      "Nearest to 里: 入, 中, 拥, 绕, 顷, 与, 户, 穴,\n",
      "Nearest to 江: 水, 淮, 溪, 湘, 潭, 泗, 潇, 波,\n",
      "Nearest to 更: 便, 又, 共, 正, 最, 径, 处, 但,\n",
      "Nearest to 玉: 琼, 金, 瑶, 宝, 冰, 阓, 瀼, 铜,\n",
      "Nearest to 西: 东, 南, 北, 晤, 奄, 斜, 浔, 荦,\n",
      "Nearest to 东: 西, 北, 薰, 巨, 糜, 急, 洛, 蹶,\n",
      "Nearest to 来: 去, 又, 今, 语, 耄, 栾, 茏, 饿,\n",
      "Nearest to 日: 夕, 月, 朝, 年, 燃, 又, 时, 怕,\n",
      "Nearest to 老: 昉, 紧, 哲, 搏, 妇, 惆, 觉, 摧,\n",
      "Nearest to 为: 与, 因, 亦, 赠, 似, 词, 共, 同,\n",
      "Average loss at step  202000 :  4.060363040208816\n",
      "Average loss at step  204000 :  4.032681112170219\n",
      "Average loss at step  206000 :  4.086918935060501\n",
      "Average loss at step  208000 :  4.059558287382126\n",
      "Average loss at step  210000 :  3.9296580946445463\n",
      "Nearest to 寒: 秋, 黏, 暝, 凉, 暖, 愁, 探, 残,\n",
      "Nearest to 年: 岁, 畴, 时, 缵, 牟, 日, 釂, 妥,\n",
      "Nearest to 不: 未, 无, 难, 技, 羞, 歃, 也, 莫,\n",
      "Nearest to 今: 昨, 此, 他, 许, 愆, 元, 明, 来,\n",
      "Nearest to 明: 皎, 晓, 光, 8, 今, 当, 映, 晴,\n",
      "Nearest to 夜: 宵, 夕, 敞, 焘, 后, 对, 峙, 更,\n",
      "Nearest to 里: 中, 入, 拥, 绕, 顷, 穴, 户, 与,\n",
      "Nearest to 江: 水, 淮, 溪, 湘, 潭, 潇, 泗, 湖,\n",
      "Nearest to 更: 便, 又, 共, 正, 处, 最, 且, x,\n",
      "Nearest to 玉: 琼, 金, 瑶, 冰, 宝, 阓, 佳, 獠,\n",
      "Nearest to 西: 东, 南, 北, 奄, 晤, 浔, 临, 斜,\n",
      "Nearest to 东: 西, 北, 巨, 糜, 薰, 险, 急, 洛,\n",
      "Nearest to 来: 去, 又, 茏, 今, 栾, 惊, 饿, 耄,\n",
      "Nearest to 日: 月, 夕, 朝, 年, 燃, 怕, 夜, 妨,\n",
      "Nearest to 老: 哲, 摧, 妇, 搏, 昉, 紧, 惆, 非,\n",
      "Nearest to 为: 与, 因, 同, 词, 共, 似, 亦, 赠,\n",
      "Average loss at step  212000 :  3.988168033361435\n",
      "Average loss at step  214000 :  3.995605481505394\n",
      "Average loss at step  216000 :  3.9547132580280304\n",
      "Average loss at step  218000 :  3.970823042333126\n",
      "Average loss at step  220000 :  4.006308270215988\n",
      "Nearest to 寒: 秋, 暝, 黏, 暖, 溶, 凉, 冷, 残,\n",
      "Nearest to 年: 岁, 畴, 缵, 时, 牟, 釂, 阅, 撑,\n",
      "Nearest to 不: 未, 难, 何, 莫, 无, 歃, 却, 滚,\n",
      "Nearest to 今: 昨, 他, 此, 许, 元, 愆, 明, 算,\n",
      "Nearest to 明: 8, 皎, 光, 照, 今, 聂, 吐, 当,\n",
      "Nearest to 夜: 宵, 夕, 焘, 敞, 对, 后, 峙, 缔,\n",
      "Nearest to 里: 入, 中, 拥, 绕, 与, 垤, 穴, 语,\n",
      "Nearest to 江: 水, 淮, 溪, 湘, 泗, 潇, 潭, 麈,\n",
      "Nearest to 更: 便, 又, 共, 但, 处, 最, 正, x,\n",
      "Nearest to 玉: 琼, 瑶, 金, 宝, 冰, 獠, 瀼, 璧,\n",
      "Nearest to 西: 东, 南, 北, 晤, 奄, 浔, 临, 荦,\n",
      "Nearest to 东: 西, 北, 巨, 糜, 急, 斜, 蹶, 险,\n",
      "Nearest to 来: 去, 今, 茏, 又, 语, 暄, 还, 耄,\n",
      "Nearest to 日: 夕, 朝, 燃, 月, 年, 又, 姥, 稷,\n",
      "Nearest to 老: 哲, 妇, 搏, 而, 昉, 稚, 摧, 惆,\n",
      "Nearest to 为: 与, 词, 因, 赠, 亦, 同, 共, 伴,\n",
      "Average loss at step  222000 :  3.9928017406463625\n",
      "Average loss at step  224000 :  4.00905119907856\n",
      "Average loss at step  226000 :  4.077461753845215\n",
      "Average loss at step  228000 :  4.05300359070301\n",
      "Average loss at step  230000 :  4.06596548974514\n",
      "Nearest to 寒: 秋, 暝, 黏, 冷, 暖, 凉, 溶, 愁,\n",
      "Nearest to 年: 岁, 畴, 缵, 日, 时, 撑, 牟, 阅,\n",
      "Nearest to 不: 未, 难, 歃, 也, 莫, 否, 技, 滚,\n",
      "Nearest to 今: 昨, 此, 他, 许, 愆, 明, 算, 元,\n",
      "Nearest to 明: 皎, 当, 榔, 今, 8, 聂, 圣, 射,\n",
      "Nearest to 夜: 宵, 夕, 焘, 敞, 后, 对, 缔, 峙,\n",
      "Nearest to 里: 入, 绕, 中, 拥, 与, 穴, 语, 顷,\n",
      "Nearest to 江: 水, 淮, 溪, 湘, 泗, 潭, 波, 潇,\n",
      "Nearest to 更: 又, 便, 共, 正, 处, 但, 最, 径,\n",
      "Nearest to 玉: 琼, 金, 瑶, 宝, 冰, 阓, 瀼, 箪,\n",
      "Nearest to 西: 东, 南, 晤, 北, 奄, 浔, 斜, 旧,\n",
      "Nearest to 东: 西, 北, 巨, 糜, 薰, 急, 蹶, 斜,\n",
      "Nearest to 来: 去, 又, 今, 耄, 茏, 语, 饿, 还,\n",
      "Nearest to 日: 夕, 月, 朝, 年, 燃, 时, 又, 怕,\n",
      "Nearest to 老: 哲, 昉, 妇, 惆, 搏, 稚, 而, 摧,\n",
      "Nearest to 为: 与, 因, 词, 赠, 亦, 似, 共, 同,\n",
      "Average loss at step  232000 :  4.038580160975457\n",
      "Average loss at step  234000 :  4.016149513602257\n",
      "Average loss at step  236000 :  4.0675053650140764\n",
      "Average loss at step  238000 :  4.0400855884552005\n",
      "Average loss at step  240000 :  3.919964237689972\n",
      "Nearest to 寒: 秋, 暝, 黏, 凉, 湫, 残, 暖, 冷,\n",
      "Nearest to 年: 岁, 畴, 时, 缵, 牟, 日, 釂, 笄,\n",
      "Nearest to 不: 未, 难, 无, 歃, 羞, 技, 也, 否,\n",
      "Nearest to 今: 昨, 此, 他, 愆, 许, 元, 来, 明,\n",
      "Nearest to 明: 皎, 光, 晓, 8, 聂, 当, 今, 晴,\n",
      "Nearest to 夜: 宵, 夕, 敞, 焘, 对, 后, 日, 更,\n",
      "Nearest to 里: 入, 中, 拥, 绕, 穴, 与, 户, 顷,\n",
      "Nearest to 江: 水, 淮, 溪, 湘, 湖, 潇, 潭, 泗,\n",
      "Nearest to 更: 便, 又, 共, 正, 最, 处, 但, 莫,\n",
      "Nearest to 玉: 琼, 金, 瑶, 冰, 宝, 佳, 阓, 獠,\n",
      "Nearest to 西: 东, 南, 北, 晤, 奄, 浔, 临, 斜,\n",
      "Nearest to 东: 西, 北, 巨, 急, 薰, 斜, 糜, 江,\n",
      "Nearest to 来: 去, 又, 茏, 今, 饿, 栾, 耄, 还,\n",
      "Nearest to 日: 月, 夕, 朝, 年, 燃, 怕, 妨, 夜,\n",
      "Nearest to 老: 哲, 摧, 妇, 搏, 惆, 昉, 而, 稚,\n",
      "Nearest to 为: 与, 因, 同, 词, 共, 亦, 似, 赠,\n",
      "Average loss at step  242000 :  3.962697362303734\n",
      "Average loss at step  244000 :  3.99122538292408\n",
      "Average loss at step  246000 :  3.943860283255577\n",
      "Average loss at step  248000 :  3.9586200523972512\n",
      "Average loss at step  250000 :  3.9767242299318313\n",
      "Nearest to 寒: 秋, 暝, 黏, 凉, 暖, 湫, 溶, 探,\n",
      "Nearest to 年: 岁, 畴, 缵, 时, 牟, 记, 釂, 日,\n",
      "Nearest to 不: 未, 难, 莫, 何, 却, 无, 歃, 滚,\n",
      "Nearest to 今: 昨, 他, 此, 许, 愆, 算, 明, 元,\n",
      "Nearest to 明: 皎, 今, 光, 聂, 8, 当, 吐, 照,\n",
      "Nearest to 夜: 宵, 夕, 焘, 敞, 对, 后, 峙, 逮,\n",
      "Nearest to 里: 入, 中, 绕, 拥, 与, 逐, 载, 语,\n",
      "Nearest to 江: 淮, 水, 溪, 湘, 泗, 岭, 潇, 潭,\n",
      "Nearest to 更: 便, 又, 共, 但, 正, 最, 处, x,\n",
      "Nearest to 玉: 琼, 金, 瑶, 冰, 宝, 獠, 璧, 瀼,\n",
      "Nearest to 西: 东, 南, 北, 奄, 晤, 浔, 临, 斜,\n",
      "Nearest to 东: 西, 北, 巨, 斜, 糜, 急, 江, 蹶,\n",
      "Nearest to 来: 去, 又, 茏, 还, 暄, 今, 耄, 语,\n",
      "Nearest to 日: 夕, 月, 朝, 年, 燃, 又, 稷, 姥,\n",
      "Nearest to 老: 哲, 妇, 搏, 惆, 摧, 而, 昉, 稚,\n",
      "Nearest to 为: 与, 因, 词, 亦, 赠, 共, 似, 伴,\n",
      "Average loss at step  252000 :  3.9835378744602203\n",
      "Average loss at step  254000 :  4.024513927698135\n",
      "Average loss at step  256000 :  4.049656466603279\n",
      "Average loss at step  258000 :  4.0415719554424285\n",
      "Average loss at step  260000 :  4.046416350841522\n",
      "Nearest to 寒: 秋, 黏, 暖, 暝, 冷, 湫, 凉, 愁,\n",
      "Nearest to 年: 岁, 缵, 畴, 日, 时, 牟, 笄, 撑,\n",
      "Nearest to 不: 未, 难, 歃, 也, 滚, 莫, 技, 否,\n",
      "Nearest to 今: 昨, 他, 此, 许, 愆, 明, 算, 往,\n",
      "Nearest to 明: 皎, 当, 射, 聂, 榔, 今, 圣, 8,\n",
      "Nearest to 夜: 宵, 夕, 焘, 敞, 后, 逮, 缔, 悄,\n",
      "Nearest to 里: 入, 拥, 中, 绕, 与, 逐, 穴, 卷,\n",
      "Nearest to 江: 水, 淮, 溪, 湘, 泗, 潭, 东, 潇,\n",
      "Nearest to 更: 又, 便, 正, 共, 最, 但, 处, 苦,\n",
      "Nearest to 玉: 琼, 金, 瑶, 宝, 冰, 阓, 瀼, 璧,\n",
      "Nearest to 西: 东, 南, 北, 晤, 浔, 奄, 斜, 临,\n",
      "Nearest to 东: 西, 北, 斜, 薰, 急, 巨, 江, 洛,\n",
      "Nearest to 来: 去, 耄, 又, 茏, 今, 饿, 语, 告,\n",
      "Nearest to 日: 夕, 朝, 月, 年, 燃, 时, 又, 妨,\n",
      "Nearest to 老: 惆, 哲, 摧, 搏, 昉, 妇, 而, 去,\n",
      "Nearest to 为: 与, 因, 词, 亦, 似, 赠, 伴, 共,\n",
      "Average loss at step  262000 :  4.01195576441288\n",
      "Average loss at step  264000 :  4.017390406966209\n",
      "Average loss at step  266000 :  4.048545530438423\n",
      "Average loss at step  268000 :  3.997049187898636\n",
      "Average loss at step  270000 :  3.937370911836624\n",
      "Nearest to 寒: 秋, 暝, 黏, 暖, 愁, 凉, 湫, 冷,\n",
      "Nearest to 年: 岁, 畴, 缵, 时, 日, 牟, 笄, 记,\n",
      "Nearest to 不: 未, 无, 难, 歃, 莫, 滚, 何, 羞,\n",
      "Nearest to 今: 昨, 此, 他, 愆, 许, 来, 它, 蒻,\n",
      "Nearest to 明: 皎, 光, 射, 晴, 聂, 晓, 吐, 8,\n",
      "Nearest to 夜: 宵, 夕, 对, 后, 焘, 敞, 逮, 日,\n",
      "Nearest to 里: 中, 入, 拥, 绕, 与, 穴, 坳, 户,\n",
      "Nearest to 江: 水, 淮, 溪, 湘, 湖, 潇, 潭, 岭,\n",
      "Nearest to 更: 便, 又, 正, 共, 最, 处, 莫, 但,\n",
      "Nearest to 玉: 琼, 金, 瑶, 冰, 宝, 璧, 阓, 瀼,\n",
      "Nearest to 西: 东, 南, 北, 奄, 浔, 晤, 临, 斜,\n",
      "Nearest to 东: 西, 北, 巨, 斜, 急, 薰, 洛, 糜,\n",
      "Nearest to 来: 去, 又, 茏, 还, 今, ，, 饿, 语,\n",
      "Nearest to 日: 夕, 月, 朝, 年, 夜, 燃, 妨, 藤,\n",
      "Nearest to 老: 哲, 惆, 摧, 妇, 搏, 昉, 非, 而,\n",
      "Nearest to 为: 与, 因, 亦, 似, 词, 赠, 共, 同,\n",
      "Average loss at step  272000 :  3.952787050008774\n",
      "Average loss at step  274000 :  3.9784359492063524\n",
      "Average loss at step  276000 :  3.9333411334753037\n",
      "Average loss at step  278000 :  3.9508924337625504\n",
      "Average loss at step  280000 :  3.9570178480148317\n",
      "Nearest to 寒: 秋, 暝, 黏, 暖, 凉, 冷, 湫, 溶,\n",
      "Nearest to 年: 岁, 缵, 畴, 时, 牟, 记, 笄, 釂,\n",
      "Nearest to 不: 未, 难, 何, 歃, 无, 却, 莫, 滚,\n",
      "Nearest to 今: 昨, 此, 他, 愆, 许, 它, 算, 明,\n",
      "Nearest to 明: 皎, 光, 照, 8, 今, 射, 聂, 吐,\n",
      "Nearest to 夜: 宵, 夕, 焘, 对, 后, 敞, 峙, 至,\n",
      "Nearest to 里: 入, 与, 中, 绕, 拥, 逐, 样, 载,\n",
      "Nearest to 江: 水, 淮, 溪, 湘, 泗, 潇, 潭, 岭,\n",
      "Nearest to 更: 便, 又, 最, 正, 共, 但, 处, 已,\n",
      "Nearest to 玉: 琼, 金, 瑶, 冰, 宝, 獠, 璧, 瀼,\n",
      "Nearest to 西: 东, 南, 北, 奄, 浔, 晤, 临, 斜,\n",
      "Nearest to 东: 西, 北, 斜, 巨, 急, 江, 蹶, 糜,\n",
      "Nearest to 来: 去, 茏, 还, 又, 暄, 娣, 语, 耄,\n",
      "Nearest to 日: 夕, 朝, 月, 燃, 年, 稷, 又, 姥,\n",
      "Nearest to 老: 哲, 妇, 搏, 惆, 摧, 而, 昉, 稚,\n",
      "Nearest to 为: 与, 因, 词, 赠, 共, 伴, 亦, 似,\n",
      "Average loss at step  282000 :  3.982419371366501\n",
      "Average loss at step  284000 :  4.023002603769302\n",
      "Average loss at step  286000 :  4.036949496626854\n",
      "Average loss at step  288000 :  4.031537332892418\n",
      "Average loss at step  290000 :  4.037573582530022\n",
      "Nearest to 寒: 秋, 黏, 暖, 冷, 暝, 湫, 愁, 凉,\n",
      "Nearest to 年: 岁, 缵, 时, 畴, 日, 笄, 撑, 牟,\n",
      "Nearest to 不: 未, 难, 歃, 也, 滚, 否, 莫, 技,\n",
      "Nearest to 今: 昨, 此, 他, 愆, 许, 它, 明, 算,\n",
      "Nearest to 明: 皎, 射, 榔, 聂, 今, 当, 沔, 光,\n",
      "Nearest to 夜: 宵, 夕, 焘, 敞, 逮, 对, 后, 缔,\n",
      "Nearest to 里: 入, 中, 绕, 拥, 与, 逐, 穴, 样,\n",
      "Nearest to 江: 水, 淮, 溪, 湘, 泗, 潭, 东, 潇,\n",
      "Nearest to 更: 又, 便, 共, 最, 处, 正, 但, 苦,\n",
      "Nearest to 玉: 琼, 金, 瑶, 宝, 冰, 阓, 箪, 璧,\n",
      "Nearest to 西: 东, 南, 北, 晤, 浔, 奄, 斜, 临,\n",
      "Nearest to 东: 西, 北, 斜, 薰, 巨, 急, 江, 蹶,\n",
      "Nearest to 来: 去, 茏, 又, 告, 还, 耄, 今, 饿,\n",
      "Nearest to 日: 夕, 朝, 月, 年, 时, 燃, 又, 稷,\n",
      "Nearest to 老: 惆, 哲, 妇, 昉, 摧, 搏, 觉, 而,\n",
      "Nearest to 为: 与, 因, 赠, 似, 亦, 词, 伴, 共,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average loss at step  292000 :  3.9922570890188216\n",
      "Average loss at step  294000 :  4.021699827313423\n",
      "Average loss at step  296000 :  4.0264337298870085\n",
      "Average loss at step  298000 :  3.9791336940526962\n",
      "Average loss at step  300000 :  3.922797718167305\n",
      "Nearest to 寒: 秋, 暝, 黏, 暖, 愁, 冷, 湫, 凉,\n",
      "Nearest to 年: 岁, 笄, 缵, 畴, 日, 时, 牟, 记,\n",
      "Nearest to 不: 未, 无, 难, 歃, 否, 何, 莫, 羞,\n",
      "Nearest to 今: 昨, 此, 他, 愆, 许, 它, 来, 亳,\n",
      "Nearest to 明: 皎, 光, 聂, 射, 晴, 晓, 润, 8,\n",
      "Nearest to 夜: 宵, 夕, 对, 后, 逮, 焘, 昼, 敞,\n",
      "Nearest to 里: 中, 入, 拥, 绕, 穴, 户, 逐, 与,\n",
      "Nearest to 江: 淮, 水, 溪, 湘, 湖, 潇, 潭, 岭,\n",
      "Nearest to 更: 便, 又, 共, 最, 莫, 正, 但, 处,\n",
      "Nearest to 玉: 琼, 金, 瑶, 冰, 阓, 宝, 獠, 璧,\n",
      "Nearest to 西: 东, 南, 北, 浔, 奄, 晤, 临, 斜,\n",
      "Nearest to 东: 西, 北, 巨, 斜, 江, 急, 蹶, 薰,\n",
      "Nearest to 来: 去, 茏, 又, 还, 今, 饿, 娣, 语,\n",
      "Nearest to 日: 夕, 月, 年, 朝, 燃, 庸, 妨, 又,\n",
      "Nearest to 老: 哲, 惆, 妇, 摧, 而, 搏, 非, 昉,\n",
      "Nearest to 为: 与, 因, 似, 亦, 共, 同, 以, 赠,\n",
      "Average loss at step  302000 :  3.9580931339263916\n",
      "Average loss at step  304000 :  3.936762622117996\n",
      "Average loss at step  306000 :  3.940189952015877\n",
      "Average loss at step  308000 :  3.9340973638892174\n",
      "Average loss at step  310000 :  3.95613416326046\n",
      "Nearest to 寒: 秋, 暝, 黏, 凉, 暖, 冷, 湫, 愁,\n",
      "Nearest to 年: 岁, 缵, 畴, 时, 记, 笄, 牟, 日,\n",
      "Nearest to 不: 未, 难, 何, 歃, 莫, 无, 却, 也,\n",
      "Nearest to 今: 昨, 此, 他, 愆, 它, 明, 许, 算,\n",
      "Nearest to 明: 皎, 今, 光, 聂, 射, 8, 照, 沔,\n",
      "Nearest to 夜: 宵, 夕, 后, 焘, 对, 至, 敞, 屿,\n",
      "Nearest to 里: 入, 拥, 中, 绕, 与, 逐, 样, 换,\n",
      "Nearest to 江: 淮, 水, 溪, 湘, 泗, 潇, 潭, 湖,\n",
      "Nearest to 更: 便, 又, 最, 但, 共, 正, 处, 濡,\n",
      "Nearest to 玉: 琼, 瑶, 金, 冰, 宝, 獠, 璧, 阓,\n",
      "Nearest to 西: 东, 南, 北, 浔, 奄, 晤, 临, 江,\n",
      "Nearest to 东: 西, 北, 巨, 江, 斜, 急, 蹶, 糜,\n",
      "Nearest to 来: 去, 茏, 还, 又, 语, 暄, 娣, 觐,\n",
      "Nearest to 日: 夕, 朝, 月, 年, 燃, 稷, 庸, 又,\n",
      "Nearest to 老: 哲, 妇, 惆, 而, 搏, 摧, 稚, 泷,\n",
      "Nearest to 为: 与, 因, 亦, 似, 词, 共, 伴, 赠,\n",
      "Average loss at step  312000 :  3.9711502048969267\n",
      "Average loss at step  314000 :  4.029213564395905\n",
      "Average loss at step  316000 :  4.0169411274194715\n",
      "Average loss at step  318000 :  4.027944444894791\n",
      "Average loss at step  320000 :  4.018018386244774\n",
      "Nearest to 寒: 秋, 黏, 暖, 暝, 冷, 湫, 凉, 愁,\n",
      "Nearest to 年: 岁, 缵, 时, 日, 笄, 畴, 记, 釂,\n",
      "Nearest to 不: 未, 难, 歃, 也, 滚, 莫, 否, 技,\n",
      "Nearest to 今: 昨, 此, 愆, 他, 许, 它, 昔, 算,\n",
      "Nearest to 明: 皎, 射, 聂, 榔, 当, 沔, 今, 吐,\n",
      "Nearest to 夜: 宵, 夕, 焘, 对, 敞, 悄, 后, 逮,\n",
      "Nearest to 里: 入, 中, 拥, 绕, 与, 逐, 样, 穴,\n",
      "Nearest to 江: 淮, 水, 溪, 湘, 泗, 潇, 东, 潭,\n",
      "Nearest to 更: 又, 便, 最, 正, 共, 但, 处, 苦,\n",
      "Nearest to 玉: 琼, 金, 瑶, 冰, 宝, 阓, 瀼, 璧,\n",
      "Nearest to 西: 东, 南, 浔, 北, 晤, 奄, 斜, 临,\n",
      "Nearest to 东: 西, 北, 斜, 薰, 江, 急, 巨, 蹶,\n",
      "Nearest to 来: 去, 茏, 告, 又, 还, 耄, 及, 今,\n",
      "Nearest to 日: 夕, 朝, 月, 年, 时, 又, 稷, 燃,\n",
      "Nearest to 老: 惆, 摧, 搏, 哲, 觉, 昉, 凋, 妇,\n",
      "Nearest to 为: 与, 因, 似, 词, 赠, 亦, 伴, 共,\n",
      "Average loss at step  322000 :  3.9840412636995315\n",
      "Average loss at step  324000 :  4.021171202778816\n",
      "Average loss at step  326000 :  4.012876617193222\n",
      "Average loss at step  328000 :  3.9367302607297896\n",
      "Average loss at step  330000 :  3.9265395609140397\n",
      "Nearest to 寒: 秋, 黏, 暝, 暖, 湫, 凉, 冷, 愁,\n",
      "Nearest to 年: 岁, 笄, 时, 畴, 缵, 日, 记, 牟,\n",
      "Nearest to 不: 未, 难, 无, 歃, 何, 羞, 也, 滚,\n",
      "Nearest to 今: 昨, 此, 愆, 他, 来, 它, 许, 亳,\n",
      "Nearest to 明: 皎, 光, 晴, 射, 聂, 润, 沔, 吐,\n",
      "Nearest to 夜: 宵, 夕, 对, 后, 昼, 逮, 焘, 敞,\n",
      "Nearest to 里: 入, 中, 拥, 绕, 与, 逐, 样, 外,\n",
      "Nearest to 江: 水, 淮, 溪, 湘, 湖, 潇, 潭, 岭,\n",
      "Nearest to 更: 便, 又, 最, 共, 但, 正, 莫, 且,\n",
      "Nearest to 玉: 琼, 金, 瑶, 冰, 阓, 瀼, 璧, 宝,\n",
      "Nearest to 西: 东, 南, 北, 浔, 奄, 晤, 临, 斜,\n",
      "Nearest to 东: 西, 北, 斜, 巨, 急, 薰, 蹶, 江,\n",
      "Nearest to 来: 去, 茏, 又, 还, 今, 饿, 娣, 惊,\n",
      "Nearest to 日: 夕, 月, 朝, 年, 庸, 燃, 夜, 妨,\n",
      "Nearest to 老: 哲, 惆, 妇, 摧, 搏, 而, 凋, 稚,\n",
      "Nearest to 为: 与, 因, 共, 亦, 词, 赠, 似, 同,\n",
      "Average loss at step  332000 :  3.965746802806854\n",
      "Average loss at step  334000 :  3.918719996213913\n",
      "Average loss at step  336000 :  3.924863177537918\n",
      "Average loss at step  338000 :  3.936023767709732\n",
      "Average loss at step  340000 :  3.9398424390554427\n",
      "Nearest to 寒: 秋, 暝, 黏, 冷, 凉, 暖, 湫, 愁,\n",
      "Nearest to 年: 岁, 缵, 畴, 时, 记, 笄, 牟, 日,\n",
      "Nearest to 不: 未, 难, 歃, 莫, 却, 何, 也, 羞,\n",
      "Nearest to 今: 昨, 此, 他, 愆, 明, 它, 算, 许,\n",
      "Nearest to 明: 皎, 光, 今, 射, 聂, 润, 照, 沔,\n",
      "Nearest to 夜: 宵, 夕, 后, 焘, 对, 至, 屿, 峙,\n",
      "Nearest to 里: 入, 与, 中, 拥, 绕, 逐, 样, 换,\n",
      "Nearest to 江: 水, 淮, 溪, 湘, 泗, 东, 潭, 潇,\n",
      "Nearest to 更: 便, 又, 最, 共, 但, 正, 已, 濡,\n",
      "Nearest to 玉: 琼, 金, 瑶, 冰, 宝, 璧, 獠, 莹,\n",
      "Nearest to 西: 东, 南, 北, 浔, 奄, 晤, 临, 斜,\n",
      "Nearest to 东: 西, 北, 斜, 江, 巨, 急, 蹶, 糜,\n",
      "Nearest to 来: 去, 又, 茏, 还, 语, 告, 觐, 娣,\n",
      "Nearest to 日: 夕, 月, 朝, 年, 燃, 稷, 首, 又,\n",
      "Nearest to 老: 哲, 妇, 惆, 而, 搏, 摧, 稚, 觉,\n",
      "Nearest to 为: 与, 因, 词, 亦, 赠, 似, 作, 共,\n",
      "Average loss at step  342000 :  3.972576246738434\n",
      "Average loss at step  344000 :  4.0144368393421175\n",
      "Average loss at step  346000 :  4.002228831887245\n",
      "Average loss at step  348000 :  4.039025424957275\n",
      "Average loss at step  350000 :  3.9976824733018876\n",
      "Nearest to 寒: 秋, 黏, 冷, 暝, 暖, 愁, 湫, 凉,\n",
      "Nearest to 年: 岁, 缵, 时, 日, 畴, 笄, 釂, 牟,\n",
      "Nearest to 不: 未, 难, 歃, 滚, 也, 否, 莫, 技,\n",
      "Nearest to 今: 昨, 此, 愆, 他, 许, 它, 算, 昔,\n",
      "Nearest to 明: 皎, 射, 榔, 聂, 今, 当, 沔, 润,\n",
      "Nearest to 夜: 宵, 夕, 焘, 昼, 后, 敞, 爽, 对,\n",
      "Nearest to 里: 入, 中, 拥, 绕, 与, 逐, 样, 穴,\n",
      "Nearest to 江: 水, 淮, 溪, 湘, 东, 泗, 湖, 潭,\n",
      "Nearest to 更: 便, 又, 最, 正, 共, 处, 但, 已,\n",
      "Nearest to 玉: 琼, 金, 瑶, 冰, 宝, 阓, 璧, 莹,\n",
      "Nearest to 西: 东, 南, 浔, 北, 晤, 奄, 斜, 临,\n",
      "Nearest to 东: 西, 北, 江, 斜, 巨, 急, 薰, 蹶,\n",
      "Nearest to 来: 去, 茏, 告, 又, 还, 归, 耄, 及,\n",
      "Nearest to 日: 夕, 朝, 年, 月, 燃, 首, 时, 稷,\n",
      "Nearest to 老: 惆, 摧, 凋, 觉, 搏, 蕖, 哲, 昉,\n",
      "Nearest to 为: 与, 因, 亦, 似, 赠, 词, 伴, 共,\n",
      "Average loss at step  352000 :  3.982126039028168\n",
      "Average loss at step  354000 :  4.009556075811386\n",
      "Average loss at step  356000 :  4.010242151975632\n",
      "Average loss at step  358000 :  3.9044131734371184\n",
      "Average loss at step  360000 :  3.936053552865982\n",
      "Nearest to 寒: 秋, 黏, 暝, 暖, 冷, 湫, 愁, 杵,\n",
      "Nearest to 年: 岁, 笄, 缵, 时, 畴, 日, 牟, 记,\n",
      "Nearest to 不: 未, 难, 无, 何, 否, 谁, 歃, 滚,\n",
      "Nearest to 今: 昨, 此, 他, 愆, 它, 许, 亳, 来,\n",
      "Nearest to 明: 皎, 光, 射, 聂, 润, 沔, 晴, 榔,\n",
      "Nearest to 夜: 宵, 夕, 后, 对, 昼, 屿, 逮, 焘,\n",
      "Nearest to 里: 入, 中, 拥, 与, 逐, 样, 绕, 穴,\n",
      "Nearest to 江: 水, 淮, 溪, 湘, 湖, 泗, 波, 潇,\n",
      "Nearest to 更: 便, 又, 最, 共, 但, 莫, 正, 且,\n",
      "Nearest to 玉: 琼, 瑶, 金, 冰, 璧, 宝, 麒, 瀼,\n",
      "Nearest to 西: 东, 南, 浔, 北, 晤, 奄, 斜, 临,\n",
      "Nearest to 东: 西, 北, 斜, 巨, 急, 蹶, 江, 薰,\n",
      "Nearest to 来: 去, 茏, 又, 还, 今, 觐, 饿, 娣,\n",
      "Nearest to 日: 夕, 年, 朝, 月, 庸, 燃, 夜, 宵,\n",
      "Nearest to 老: 摧, 哲, 妇, 惆, 搏, 凋, 蕖, 非,\n",
      "Nearest to 为: 与, 因, 亦, 共, 同, 赠, 以, 词,\n",
      "Average loss at step  362000 :  3.9497845668792726\n",
      "Average loss at step  364000 :  3.8995778777599335\n",
      "Average loss at step  366000 :  3.9284147160053253\n",
      "Average loss at step  368000 :  3.943875606238842\n",
      "Average loss at step  370000 :  3.924301012277603\n",
      "Nearest to 寒: 秋, 黏, 暝, 凉, 冷, 湫, 暖, 愁,\n",
      "Nearest to 年: 岁, 缵, 畴, 时, 牟, 记, 笄, 取,\n",
      "Nearest to 不: 未, 难, 歃, 莫, 也, 却, 何, 羞,\n",
      "Nearest to 今: 昨, 此, 他, 愆, 它, 算, 明, 许,\n",
      "Nearest to 明: 皎, 射, 光, 聂, 润, 轺, 今, 沔,\n",
      "Nearest to 夜: 宵, 夕, 后, 焘, 对, 至, 屿, 峙,\n",
      "Nearest to 里: 入, 与, 拥, 中, 样, 绕, 逐, 坊,\n",
      "Nearest to 江: 淮, 水, 溪, 泗, 东, 湘, 潇, 湖,\n",
      "Nearest to 更: 便, 又, 最, 共, 但, 正, 已, 处,\n",
      "Nearest to 玉: 琼, 瑶, 金, 冰, 宝, 璧, 莹, 獠,\n",
      "Nearest to 西: 东, 南, 北, 浔, 奄, 晤, 临, 斜,\n",
      "Nearest to 东: 西, 北, 斜, 江, 巨, 急, 王, 蹶,\n",
      "Nearest to 来: 去, 茏, 觐, 蚓, 娣, 还, 又, 告,\n",
      "Nearest to 日: 夕, 朝, 月, 燃, 年, 稷, 又, 夜,\n",
      "Nearest to 老: 哲, 妇, 而, 惆, 稚, 搏, 泷, 非,\n",
      "Nearest to 为: 与, 因, 词, 作, 亦, 赠, 共, 婶,\n",
      "Average loss at step  372000 :  3.9626226642131805\n",
      "Average loss at step  374000 :  4.008927286505699\n",
      "Average loss at step  376000 :  3.9891300029754637\n",
      "Average loss at step  378000 :  4.019547744989395\n",
      "Average loss at step  380000 :  4.004320064425468\n",
      "Nearest to 寒: 秋, 黏, 暖, 冷, 暝, 湫, 愁, 凉,\n",
      "Nearest to 年: 岁, 缵, 时, 笄, 日, 畴, 釂, 记,\n",
      "Nearest to 不: 未, 难, 歃, 否, 莫, 也, 滚, 技,\n",
      "Nearest to 今: 昨, 此, 愆, 他, 它, 许, 明, 昔,\n",
      "Nearest to 明: 皎, 今, 射, 当, 润, 榔, 聂, 沔,\n",
      "Nearest to 夜: 宵, 夕, 焘, 后, 昼, 悄, 对, 爽,\n",
      "Nearest to 里: 入, 拥, 中, 与, 绕, 逐, 样, 外,\n",
      "Nearest to 江: 淮, 水, 溪, 东, 泗, 湘, 湖, 潇,\n",
      "Nearest to 更: 又, 便, 最, 正, 处, 共, 但, 已,\n",
      "Nearest to 玉: 琼, 金, 瑶, 宝, 冰, 阓, 璧, 瀼,\n",
      "Nearest to 西: 东, 南, 浔, 北, 斜, 晤, 奄, 旧,\n",
      "Nearest to 东: 西, 北, 江, 斜, 巨, 急, 薰, 蹶,\n",
      "Nearest to 来: 去, 茏, 又, 告, 及, 还, 归, 贷,\n",
      "Nearest to 日: 夕, 朝, 年, 月, 时, 首, 又, 燃,\n",
      "Nearest to 老: 惆, 觉, 摧, 凋, 搏, 哲, 蕖, 铎,\n",
      "Nearest to 为: 与, 因, 赠, 词, 亦, 似, 伴, 共,\n",
      "Average loss at step  382000 :  3.9743902525901795\n",
      "Average loss at step  384000 :  4.00416827917099\n",
      "Average loss at step  386000 :  3.998403659462929\n",
      "Average loss at step  388000 :  3.8893987205028533\n",
      "Average loss at step  390000 :  3.922774769067764\n",
      "Nearest to 寒: 秋, 黏, 暝, 暖, 冷, 湫, 杵, 愁,\n",
      "Nearest to 年: 岁, 时, 笄, 缵, 畴, 记, 牟, 日,\n",
      "Nearest to 不: 未, 难, 无, 何, 歃, 莫, 却, 滚,\n",
      "Nearest to 今: 昨, 此, 愆, 他, 来, 它, 明, 蒻,\n",
      "Nearest to 明: 皎, 光, 射, 润, 聂, 晴, 今, 轺,\n",
      "Nearest to 夜: 宵, 夕, 后, 对, 昼, 至, 焘, 悄,\n",
      "Nearest to 里: 入, 中, 拥, 样, 与, 逐, 外, 绕,\n",
      "Nearest to 江: 淮, 水, 溪, 湖, 湘, 泗, 东, 潭,\n",
      "Nearest to 更: 便, 又, 最, 共, 但, 正, 莫, 已,\n",
      "Nearest to 玉: 琼, 金, 瑶, 冰, 璧, 麒, 阓, 佳,\n",
      "Nearest to 西: 东, 南, 浔, 北, 奄, 晤, 斜, 临,\n",
      "Nearest to 东: 西, 北, 斜, 巨, 江, 急, 蹶, 南,\n",
      "Nearest to 来: 去, 茏, 又, 今, 还, 觐, 娣, 起,\n",
      "Nearest to 日: 夕, 月, 朝, 年, 燃, 庸, 首, 夜,\n",
      "Nearest to 老: 哲, 摧, 妇, 惆, 凋, 搏, 非, 死,\n",
      "Nearest to 为: 与, 因, 共, 亦, 同, 向, 以, 等,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average loss at step  392000 :  3.9620354155302047\n",
      "Average loss at step  394000 :  3.892773234963417\n",
      "Average loss at step  396000 :  3.9227176533937453\n",
      "Average loss at step  398000 :  3.9297580737471582\n",
      "Average loss at step  400000 :  3.924734432220459\n",
      "Nearest to 寒: 秋, 黏, 暝, 冷, 湫, 凉, 暖, 愁,\n",
      "Nearest to 年: 岁, 缵, 畴, 时, 记, 牟, 笄, 日,\n",
      "Nearest to 不: 未, 难, 歃, 莫, 却, 羞, 也, 技,\n",
      "Nearest to 今: 昨, 此, 愆, 他, 它, 算, 来, 明,\n",
      "Nearest to 明: 皎, 射, 聂, 光, 润, 轺, 今, 吐,\n",
      "Nearest to 夜: 宵, 夕, 焘, 后, 屿, 对, 逮, 峙,\n",
      "Nearest to 里: 入, 与, 拥, 样, 绕, 逐, 中, 载,\n",
      "Nearest to 江: 淮, 水, 溪, 东, 泗, 湘, 潭, 潇,\n",
      "Nearest to 更: 便, 又, 最, 但, 共, 正, 已, 处,\n",
      "Nearest to 玉: 琼, 金, 瑶, 冰, 璧, 宝, 莹, 槊,\n",
      "Nearest to 西: 东, 南, 北, 浔, 奄, 晤, 临, 仙,\n",
      "Nearest to 东: 西, 北, 斜, 江, 巨, 急, 王, 蹶,\n",
      "Nearest to 来: 去, 茏, 告, 今, 觐, 蚓, 又, 娣,\n",
      "Nearest to 日: 夕, 朝, 月, 年, 燃, 稷, 杲, 又,\n",
      "Nearest to 老: 哲, 妇, 惆, 稚, 非, 而, 搏, 泷,\n",
      "Nearest to 为: 与, 因, 词, 赠, 作, 亦, 以, 婶,\n"
     ]
    }
   ],
   "source": [
    "# Step 5: Begin training.\n",
    "num_steps = 400001\n",
    "with tf.Session(graph=graph) as session:\n",
    "  # We must initialize all variables before we use them.\n",
    "  init.run()\n",
    "  print('Initialized')\n",
    "\n",
    "  average_loss = 0\n",
    "  for step in xrange(num_steps):\n",
    "    batch_inputs, batch_labels = generate_batch(\n",
    "        batch_size, num_skips, skip_window)\n",
    "    feed_dict = {train_inputs: batch_inputs, train_labels: batch_labels}\n",
    "\n",
    "    # We perform one update step by evaluating the optimizer op (including it\n",
    "    # in the list of returned values for session.run()\n",
    "    _, loss_val = session.run([optimizer, loss], feed_dict=feed_dict)\n",
    "    average_loss += loss_val\n",
    "\n",
    "    if step % 2000 == 0:\n",
    "      if step > 0:\n",
    "        average_loss /= 2000\n",
    "      # The average loss is an estimate of the loss over the last 2000 batches.\n",
    "      print('Average loss at step ', step, ': ', average_loss)\n",
    "      average_loss = 0\n",
    "\n",
    "    # Note that this is expensive (~20% slowdown if computed every 500 steps)\n",
    "    if step % 10000 == 0:\n",
    "      sim = similarity.eval()\n",
    "      for i in xrange(valid_size):\n",
    "        valid_word = reverse_dictionary[valid_examples[i]]\n",
    "        top_k = 8  # number of nearest neighbors\n",
    "        nearest = (-sim[i, :]).argsort()[1:top_k + 1]\n",
    "        log_str = 'Nearest to %s:' % valid_word\n",
    "        for k in xrange(top_k):\n",
    "          close_word = reverse_dictionary[nearest[k]]\n",
    "          log_str = '%s %s,' % (log_str, close_word)\n",
    "        print(log_str)\n",
    "  final_embeddings = normalized_embeddings.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x1296 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Step 6: Visualize the embeddings.\n",
    "# pylint: disable=missing-docstring\n",
    "# Function to draw visualization of distance between embeddings.\n",
    "def plot_with_labels(low_dim_embs, labels, filename):\n",
    "    assert low_dim_embs.shape[0] >= len(labels), 'More labels than embeddings'\n",
    "    plt.figure(figsize=(18, 18))  # in inches\n",
    "    for i, label in enumerate(labels):\n",
    "        x, y = low_dim_embs[i, :]\n",
    "        plt.scatter(x, y)\n",
    "        plt.annotate(label,\n",
    "                 xy=(x, y),\n",
    "                 xytext=(5, 2),\n",
    "                 textcoords='offset points',\n",
    "                 ha='right',\n",
    "                 va='bottom')\n",
    "\n",
    "    plt.savefig(filename)\n",
    "\n",
    "\n",
    "# pylint: disable=g-import-not-at-top\n",
    "from sklearn.manifold import TSNE\n",
    "import matplotlib.pyplot as plt\n",
    "from pylab import mpl\n",
    "mpl.rcParams['font.sans-serif'] = ['SimHei']  \n",
    "mpl.rcParams['axes.unicode_minus'] = False \n",
    "\n",
    "tsne = TSNE(perplexity=30, n_components=2, init='pca', n_iter=5000, method='exact')\n",
    "plot_only = 500\n",
    "low_dim_embs = tsne.fit_transform(final_embeddings[:plot_only, :])\n",
    "labels = [reverse_dictionary[i] for i in xrange(plot_only)]\n",
    "plot_with_labels(low_dim_embs, labels, os.path.join(gettempdir(), 'tsne.png'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save('embedding.npy', final_embeddings)\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "with open('dictionary.json', 'w',encoding=\"utf-8\") as f:\n",
    "    json.dump(dictionary, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "with open('reverse_dictionary.json', 'w',encoding=\"utf-8\") as f:\n",
    "    json.dump(reverse_dictionary, f)"
   ]
  }
 ],
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